The field of the invention is systems and methods for magnetic resonance imaging (“MRI”). More particularly, the invention relates to systems and method for accelerated MRI and parallel imaging, including simultaneous multislice imaging (“SMS”).
MRI scans can often times be lengthy and, thus, not feasible for clinical application. These otherwise lengthy scans can have their scan time significantly reduced by implementing parallel imaging (“PI”) techniques to accelerate the data acquisition process. The data acquisition process is typically accelerated by undersampling k-space, such as by skipping phase-encoding or partition-encoding lines.
Typically, PI techniques make use of an array of radio frequency (“RF”) receiver coils. Each coil in the array has a unique spatial sensitivity profile, which can be utilized to supplement the spatial encoding of magnetic resonance signals and to remove aliasing artifacts caused by undersampling k-space.
There is a growing trend towards using large coil arrays with PI techniques because larger coil arrays allow greater acceleration of the data acquisition while also increasing the attainable signal-to-noise ratio. These larger coil array are not without their drawbacks, however. For instance, the computational cost of many PI algorithms scales with the square of the number of channels in the coil array, thereby leading to long reconstruction times when larger coil arrays are used. This increase in reconstruction time creates a strong incentive to reduce the effective number of channels used for PI.
Coil compression techniques can be used to reduce the computation burden of working with larger coil arrays. In general, both hardware coil compression and software-based coil compression can be used to linearly combine the data acquired on multiple coils into a reduced number of virtual coils.
Several techniques for software-based coil compression have been developed so far, with most algorithms being configured for use with two-dimensional Cartesian acquisitions, the geometric-decomposition coil compression (“GCC”) method recently proposed by T. Zhang, et al., in “Coil Compression for Accelerated Imaging with Cartesian Sampling,” Magn Reson Med, 2013; 69(2):571-582. Other methods can be implemented for a wider range of sampling patterns. For example, global mapping methods, such as SVD compression, are applicable to a wide range of k-space sampling patterns. These global mapping methods suffer from low SNR retention at high coil compression rate, however.
In light of the foregoing, there remains a need for a coil compression technique that can be widely applied to different sampling patterns (e.g., both Cartesian and non-Cartesian sampling) without loss of SNR at high coil compression rates.